Let us consider this data. The id is that of a Subject who is in
a room where monitoring equipment gathers some data. There are several visits to gather this data.

id

visit

room

value

timepoint

14

0

bedroom

6

53

14

0

bedroom

6

54

15

0

bedroom

2.75

56

The idea that this code is based on is from Martin Fowler’s book Analysis Patterns Reusable Object Models. The chapter on Observations and Measurements has a diagram roughly equivalent to the
one shown at the top.

Measurement encapsulates the quantity, the time point and the visit number. So, for example, during visit 0, at this time point the quantity was observed. This type of encapsulation in the true spirit of OO has its
disadvantages as we will see later.

LongitudinalDatum is the class LongitudinalData inherits from. This inheritance is shown as an example. Not all methods that should belong in the super class are properly added. There are many methods in the sub class that can be moved a level up.

subsummary in the super class can be called from the sub class. The line if( subject(x) == id){ in the sub class LongitudinalData calls this super class method.

How does this work ?

The data is loaded into an object hierarchy in the load function. I did observe that it was slow most probably because my Eclipse StatET for R setup needs more memory.

Since the methods are all encapsulated by the class I am using the reference to call methods. The result of findsubject is passed to subjectsummary because I am piping the result of one method to the next.

So here the result of findsubject(14) is passed as the first parameter when visit(0) is called. 0 becomes the second parameter.

out <- ld$findsubject(14) %>% ld$visit(0) %>% ld$room("bedroom")

The final result from this pipeline is whatever is returned by the last method room("bedroom").

I would like to reassert that this is just one way of combining multiple methods using Reference classes. There are much more powerful functional approaches that don’t require this many lines of code. This example illustrates a particular Object-oriented approach.

Flattening the Reference classes

The OO hierarchy here does not seem to be malleable when used with some R packagea like dplyr. Try as I may, I cannot coerce the Reference classes into a R data frame and pipe it through stages using dplyr. Remember I want to use functions like map and filter to get the data out of these reference clasees in a shape that I want.

So I abandon my OO approach and flatten the objects and create a data frame. Now I get back the data in the shape I want.

Conclusion

This exercise has not helped me determine in which context R’s Reference classes are specifically used. The other OO systems like S3 and S4 may be more useful but this article is about RC’s. Why should I flatten my object hierarchy to reshape my data in a convenient way ? There may be specialized R packages that use the OO approach and expose API’s but I am not aware of them. So at this time I understand that there is a dichotomy between RC’s and the powerful functional approach. I personally like to use the functional programming paradigm when dealing with data.

I spent a Sunday on this code to answer some questions for a Coursera course. At this time this code is the norm in more than one such course. So I am just building muscle memory. I type this code and look at the result and learn what I learnt earlier.

If I don’t remember how to solve it I search but the point is that I have to be constantly in touch with “R” as well the fundamentals. My day job doesn’t let me do this. The other option is a book on Machine Learning like the one by Tom Mitchell but that takes foreover.

I submitted a fresh pull request to adoptopenjdk to rectify the graphs in parsing-java-micro-benchmarking-harness-data-using-dplyr-part-2 after the lead developer of gs-collections identified that the titles in the two graphs are switched.

I have been researching this seemingly simple topic for many months. After reading many articles and browsing books and asking other experts I have a basic idea about this. Now I also find it ludicrous that our famed project managers in my companies do not seem to know this simple distribution after appearing for the PMP exams. Many managers here care not a coot for such technical items.

I found this blog uses ‘R’ to parse the G1 gc logs. I am specifically using ‘Format 2’ log lines mentioned in the blog. This is one of my favorite techniques to learn ‘R’. The author uses Unix shell scripts to parse the data but I think ‘R’ to an excellent language to parse data. I have done this in the past.

Eventhough my code here is verbose a ‘R’ expert can make short work of the data easily. My code uses regular expressions too many times.